Achieving an Accurate Random Process Model for PV Power using Cheap
Data: Leveraging the SDE and Public Weather Reports
- URL: http://arxiv.org/abs/2111.13812v1
- Date: Sat, 27 Nov 2021 04:34:02 GMT
- Title: Achieving an Accurate Random Process Model for PV Power using Cheap
Data: Leveraging the SDE and Public Weather Reports
- Authors: Yiwei Qiu (1), Jin Lin (2), Zhipeng Zhou (3), Ningyi Dai (3), Feng Liu
(2), Yonghua Song (3 and 2) ((1) College of Electrical Engineering, Sichuan
University, (2) State Key Laboratory of the Control and Simulation of Power
Systems and Generation Equipment, Tsinghua University, (3) State Key
Laboratory of Internet of Things for Smart City, University of Macau)
- Abstract summary: An accurate SDE model for PV power can be constructed by only using the cheap data from low-resolution public weather reports.
The proposed approach outperforms a selection of state-of-the-art deep learning-based time-series forecast methods.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The stochastic differential equation (SDE)-based random process models of
volatile renewable energy sources (RESs) jointly capture the evolving
probability distribution and temporal correlation in continuous time. It has
enabled recent studies to remarkably improve the performance of power system
dynamic uncertainty quantification and optimization. However, considering the
non-homogeneous random process nature of PV, there still remains a challenging
question: how can a realistic and accurate SDE model for PV power be obtained
that reflects its weather-dependent uncertainty in online operation, especially
when high-resolution numerical weather prediction (NWP) is unavailable for many
distributed plants? To fill this gap, this article finds that an accurate SDE
model for PV power can be constructed by only using the cheap data from
low-resolution public weather reports. Specifically, an hourly parameterized
Jacobi diffusion process is constructed to recreate the temporal patterns of PV
volatility during a day. Its parameters are mapped from the public weather
report using an ensemble of extreme learning machines (ELMs) to reflect the
varying weather conditions. The SDE model jointly captures intraday and
intrahour volatility. Statistical examination based on real-world data
collected in Macau shows the proposed approach outperforms a selection of
state-of-the-art deep learning-based time-series forecast methods.
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